A Deep Stochastic Adaptive Fourier Decomposition Network With Back-Propagation for Hyperspectral Image Classification

Convolutional neural networks (CNNs) have shown impressive performance in hyperspectral image (HSI) classification. However, these deep learning methods still face two major challenges. One is that they require a large number of training samples to train parameters, and the other is that high-dimensional nonlinear feature extraction and multisource information fusion. This article proposes a deep stochastic adaptive Fourier decomposition (SAFD) network integrated with back-propagation (BP) and multiscale feature fusion to significantly improve classification accuracy. The main contributions are threefold: 1) a deep SAFD network with BP is designed, which introduces BP into the deep SAFD network for the first time, and achieves automatic dynamic optimization of network parameters through the BP algorithm; 2) a Kalman filter-based multiscale pyramid construction method is proposed, which extracts hierarchical spatial features through state recursion equations and enhances texture representation by fusing local binary patterns (LBPs); and 3) an efficient classification algorithm based on a deep SAFD network with a BP algorithm is developed to integrate deep SAFD features, multiscale pyramid features, and LBP texture features, achieving higher classification accuracy. Experimental results show that the proposed method outperforms other selected HSI classification methods with similar principles. Moreover, compared with other state-of-the-art deep learning methods, the proposed method can achieve better classification performance.